Overview

Dataset statistics

Number of variables11
Number of observations10000000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory839.2 MiB
Average record size in memory88.0 B

Variable types

Numeric10
Categorical1

Alerts

date_index is highly overall correlated with tr_idHigh correlation
probe_efficiency is highly overall correlated with rtt_count and 2 other fieldsHigh correlation
rtt_count is highly overall correlated with probe_efficiency and 2 other fieldsHigh correlation
rtt_range is highly overall correlated with probe_efficiency and 2 other fieldsHigh correlation
rtt_std is highly overall correlated with probe_efficiency and 2 other fieldsHigh correlation
tr_id is highly overall correlated with date_indexHigh correlation
route_changed is highly imbalanced (85.5%)Imbalance
rtt_std is highly skewed (γ1 = 20.26983061)Skewed
time_since_last is highly skewed (γ1 = 244.4256114)Skewed
tr_id is uniformly distributedUniform
tr_id has unique valuesUnique
rtt_std has 9297044 (93.0%) zerosZeros
rtt_range has 9296183 (93.0%) zerosZeros
time_since_last has 9642814 (96.4%) zerosZeros

Reproduction

Analysis started2025-11-25 21:29:23.804447
Analysis finished2025-11-25 21:34:22.215420
Duration4 minutes and 58.41 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

tr_id
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct10000000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4999999.5
Minimum0
Maximum9999999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size76.3 MiB
2025-11-25T21:34:22.351948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile499999.95
Q12499999.8
median4999999.5
Q37499999.2
95-th percentile9499999
Maximum9999999
Range9999999
Interquartile range (IQR)4999999.5

Descriptive statistics

Standard deviation2886751.5
Coefficient of variation (CV)0.57735036
Kurtosis-1.2
Mean4999999.5
Median Absolute Deviation (MAD)2500000
Skewness4.9533963 × 10-17
Sum4.9999995 × 1013
Variance8.3333342 × 1012
MonotonicityNot monotonic
2025-11-25T21:34:22.508737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32627561
 
< 0.1%
32627551
 
< 0.1%
32627541
 
< 0.1%
32627531
 
< 0.1%
32627521
 
< 0.1%
32627511
 
< 0.1%
32627501
 
< 0.1%
32627491
 
< 0.1%
32627481
 
< 0.1%
32627471
 
< 0.1%
Other values (9999990)9999990
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
99999991
< 0.1%
99999981
< 0.1%
99999971
< 0.1%
99999961
< 0.1%
99999951
< 0.1%
99999941
< 0.1%
99999931
< 0.1%
99999921
< 0.1%
99999911
< 0.1%
99999901
< 0.1%

route_changed
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size476.8 MiB
0
9794065 
1
 
205935

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09794065
97.9%
1205935
 
2.1%

Length

2025-11-25T21:34:22.646941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T21:34:23.076366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
09794065
97.9%
1205935
 
2.1%

Most occurring characters

ValueCountFrequency (%)
09794065
97.9%
1205935
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10000000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09794065
97.9%
1205935
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common10000000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09794065
97.9%
1205935
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII10000000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09794065
97.9%
1205935
 
2.1%

date_index
Real number (ℝ)

High correlation 

Distinct42
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.32461
Minimum1
Maximum66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.3 MiB
2025-11-25T21:34:23.163084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q125
median43
Q352
95-th percentile64
Maximum66
Range65
Interquartile range (IQR)27

Descriptive statistics

Standard deviation20.079361
Coefficient of variation (CV)0.52392866
Kurtosis-0.79871737
Mean38.32461
Median Absolute Deviation (MAD)13
Skewness-0.62542635
Sum3.832461 × 108
Variance403.18076
MonotonicityNot monotonic
2025-11-25T21:34:23.296966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
3358706
 
3.6%
4356342
 
3.6%
2352341
 
3.5%
1344452
 
3.4%
24337864
 
3.4%
10317628
 
3.2%
25314066
 
3.1%
49283426
 
2.8%
31279400
 
2.8%
50277772
 
2.8%
Other values (32)6778003
67.8%
ValueCountFrequency (%)
1344452
3.4%
2352341
3.5%
3358706
3.6%
4356342
3.6%
5184526
1.8%
10317628
3.2%
1186005
 
0.9%
2362004
 
0.6%
24337864
3.4%
25314066
3.1%
ValueCountFrequency (%)
66177014
1.8%
65220131
2.2%
64221020
2.2%
63221093
2.2%
62233373
2.3%
61230163
2.3%
60231457
2.3%
59229184
2.3%
58235629
2.4%
57235287
2.4%

rtt_mean
Real number (ℝ)

Distinct36730
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.56296
Minimum0.14
Maximum4905.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.3 MiB
2025-11-25T21:34:23.447796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.14
5-th percentile36.27
Q163.22
median113.3
Q3137.16
95-th percentile253.4
Maximum4905.92
Range4905.78
Interquartile range (IQR)73.94

Descriptive statistics

Standard deviation78.099715
Coefficient of variation (CV)0.62698986
Kurtosis69.426593
Mean124.56296
Median Absolute Deviation (MAD)48.85
Skewness2.8911233
Sum1.2456296 × 109
Variance6099.5655
MonotonicityNot monotonic
2025-11-25T21:34:23.595378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45.3642669
 
0.4%
45.3434311
 
0.3%
45.3734143
 
0.3%
45.3833179
 
0.3%
45.3532164
 
0.3%
63.1829353
 
0.3%
92.6228800
 
0.3%
63.228230
 
0.3%
92.6427778
 
0.3%
63.1627342
 
0.3%
Other values (36720)9682031
96.8%
ValueCountFrequency (%)
0.1499
< 0.1%
0.1533
 
< 0.1%
0.1633
 
< 0.1%
0.1733
 
< 0.1%
0.18132
< 0.1%
0.1933
 
< 0.1%
0.233
 
< 0.1%
0.2199
< 0.1%
0.22132
< 0.1%
0.24198
< 0.1%
ValueCountFrequency (%)
4905.9218
< 0.1%
4293.0620
< 0.1%
3756.4221
< 0.1%
3076.6614
< 0.1%
2872.3520
< 0.1%
2804.7419
< 0.1%
2412.3818
< 0.1%
1963.6321
< 0.1%
1771.7133
< 0.1%
1449.9810
 
< 0.1%

rtt_std
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct8047
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1806685
Minimum0
Maximum1352.71
Zeros9297044
Zeros (%)93.0%
Negative0
Negative (%)0.0%
Memory size76.3 MiB
2025-11-25T21:34:23.733898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.92
Maximum1352.71
Range1352.71
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16.753856
Coefficient of variation (CV)7.6828989
Kurtosis618.31546
Mean2.1806685
Median Absolute Deviation (MAD)0
Skewness20.269831
Sum21806685
Variance280.69169
MonotonicityNot monotonic
2025-11-25T21:34:23.910698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09297044
93.0%
0.0710274
 
0.1%
0.19894
 
0.1%
0.089821
 
0.1%
0.069808
 
0.1%
0.059566
 
0.1%
0.099459
 
0.1%
0.118718
 
0.1%
0.048010
 
0.1%
0.127345
 
0.1%
Other values (8037)620061
 
6.2%
ValueCountFrequency (%)
09297044
93.0%
0.013661
 
< 0.1%
0.025980
 
0.1%
0.037181
 
0.1%
0.048010
 
0.1%
0.059566
 
0.1%
0.069808
 
0.1%
0.0710274
 
0.1%
0.089821
 
0.1%
0.099459
 
0.1%
ValueCountFrequency (%)
1352.7117
< 0.1%
1107.9118
< 0.1%
918.0320
< 0.1%
642.9110
< 0.1%
633.038
 
< 0.1%
623.6912
< 0.1%
622.1110
< 0.1%
619.879
< 0.1%
619.7410
< 0.1%
613.619
< 0.1%

rtt_range
Real number (ℝ)

High correlation  Zeros 

Distinct10611
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5439869
Minimum0
Maximum3630.08
Zeros9296183
Zeros (%)93.0%
Negative0
Negative (%)0.0%
Memory size76.3 MiB
2025-11-25T21:34:24.080657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.46
Maximum3630.08
Range3630.08
Interquartile range (IQR)0

Descriptive statistics

Standard deviation38.678707
Coefficient of variation (CV)6.9766953
Kurtosis591.75809
Mean5.5439869
Median Absolute Deviation (MAD)0
Skewness18.011129
Sum55439869
Variance1496.0424
MonotonicityNot monotonic
2025-11-25T21:34:24.225281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09296183
93.0%
0.163970
 
< 0.1%
0.153772
 
< 0.1%
0.243703
 
< 0.1%
0.123677
 
< 0.1%
0.113623
 
< 0.1%
0.233587
 
< 0.1%
0.223577
 
< 0.1%
0.193504
 
< 0.1%
0.143483
 
< 0.1%
Other values (10601)670921
 
6.7%
ValueCountFrequency (%)
09296183
93.0%
0.011611
 
< 0.1%
0.021793
 
< 0.1%
0.032067
 
< 0.1%
0.042254
 
< 0.1%
0.052994
 
< 0.1%
0.062860
 
< 0.1%
0.072325
 
< 0.1%
0.083235
 
< 0.1%
0.092525
 
< 0.1%
ValueCountFrequency (%)
3630.0817
< 0.1%
3012.4718
< 0.1%
2383.9333
< 0.1%
1836.0620
< 0.1%
1451.3910
 
< 0.1%
1285.8310
 
< 0.1%
1272.7910
 
< 0.1%
1266.159
 
< 0.1%
1266.068
 
< 0.1%
1249.129
 
< 0.1%

rtt_count
Real number (ℝ)

High correlation 

Distinct73
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5494145
Minimum1
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.3 MiB
2025-11-25T21:34:24.373826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile5
Maximum77
Range76
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.950475
Coefficient of variation (CV)1.9042515
Kurtosis151.26332
Mean1.5494145
Median Absolute Deviation (MAD)0
Skewness10.488764
Sum15494145
Variance8.7053025
MonotonicityNot monotonic
2025-11-25T21:34:24.525570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19295245
93.0%
799742
 
1.0%
696547
 
1.0%
563626
 
0.6%
457614
 
0.6%
256867
 
0.6%
352886
 
0.5%
1243015
 
0.4%
835282
 
0.4%
1132629
 
0.3%
Other values (63)166547
 
1.7%
ValueCountFrequency (%)
19295245
93.0%
256867
 
0.6%
352886
 
0.5%
457614
 
0.6%
563626
 
0.6%
696547
 
1.0%
799742
 
1.0%
835282
 
0.4%
931626
 
0.3%
1031834
 
0.3%
ValueCountFrequency (%)
776
 
< 0.1%
7615
 
< 0.1%
7519
 
< 0.1%
7215
 
< 0.1%
696
 
< 0.1%
6836
 
< 0.1%
6714
 
< 0.1%
6639
< 0.1%
6528
 
< 0.1%
6490
< 0.1%

time_since_last
Real number (ℝ)

Skewed  Zeros 

Distinct1858
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.378246
Minimum0
Maximum3802147
Zeros9642814
Zeros (%)96.4%
Negative0
Negative (%)0.0%
Memory size76.3 MiB
2025-11-25T21:34:24.669128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3802147
Range3802147
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5329.5091
Coefficient of variation (CV)92.883792
Kurtosis89667.465
Mean57.378246
Median Absolute Deviation (MAD)0
Skewness244.42561
Sum5.7378246 × 108
Variance28403667
MonotonicityNot monotonic
2025-11-25T21:34:24.815241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09642814
96.4%
660261611
 
2.6%
6593973
 
< 0.1%
6613887
 
< 0.1%
13201907
 
< 0.1%
6931701
 
< 0.1%
6681678
 
< 0.1%
6761658
 
< 0.1%
6521494
 
< 0.1%
6941395
 
< 0.1%
Other values (1848)77882
 
0.8%
ValueCountFrequency (%)
09642814
96.4%
1911
 
< 0.1%
1941
 
< 0.1%
2581
 
< 0.1%
2671
 
< 0.1%
2761
 
< 0.1%
2941
 
< 0.1%
3141
 
< 0.1%
3461
 
< 0.1%
3603
 
< 0.1%
ValueCountFrequency (%)
38021471
< 0.1%
38021251
< 0.1%
19233581
< 0.1%
18914471
< 0.1%
18462001
< 0.1%
18232201
< 0.1%
17682691
< 0.1%
17241391
< 0.1%
17111361
< 0.1%
16918801
< 0.1%

reply_ratio
Real number (ℝ)

Distinct141
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99196163
Minimum0.33
Maximum2.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.3 MiB
2025-11-25T21:34:24.960299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.33
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum2.29
Range1.96
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.090623265
Coefficient of variation (CV)0.091357632
Kurtosis43.570788
Mean0.99196163
Median Absolute Deviation (MAD)0
Skewness-2.2232494
Sum9919616.3
Variance0.0082125762
MonotonicityNot monotonic
2025-11-25T21:34:25.125754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19731264
97.3%
0.5148414
 
1.5%
0.3351768
 
0.5%
1.7112830
 
0.1%
0.886395
 
0.1%
1.53837
 
< 0.1%
1.572969
 
< 0.1%
0.672181
 
< 0.1%
0.782061
 
< 0.1%
1.831589
 
< 0.1%
Other values (131)36692
 
0.4%
ValueCountFrequency (%)
0.3351768
 
0.5%
0.4690
 
< 0.1%
0.5148414
1.5%
0.55363
 
< 0.1%
0.5611
 
< 0.1%
0.5729
 
< 0.1%
0.6513
 
< 0.1%
0.6427
 
< 0.1%
0.672181
 
< 0.1%
0.71112
 
< 0.1%
ValueCountFrequency (%)
2.2917
 
< 0.1%
2.266
 
< 0.1%
2.234
 
< 0.1%
2.1746
 
< 0.1%
2.1680
< 0.1%
2.155
 
< 0.1%
2.1413
 
< 0.1%
2.12144
< 0.1%
2.1148
 
< 0.1%
2.122
 
< 0.1%

probe_efficiency
Real number (ℝ)

High correlation 

Distinct73
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51336885
Minimum0.33
Maximum25.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size76.3 MiB
2025-11-25T21:34:25.276001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.33
5-th percentile0.33
Q10.33
median0.33
Q30.33
95-th percentile1.67
Maximum25.67
Range25.34
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.98403603
Coefficient of variation (CV)1.9168207
Kurtosis151.05589
Mean0.51336885
Median Absolute Deviation (MAD)0
Skewness10.48036
Sum5133688.5
Variance0.9683269
MonotonicityNot monotonic
2025-11-25T21:34:25.432124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.339295245
93.0%
2.3399742
 
1.0%
296547
 
1.0%
1.6763626
 
0.6%
1.3357614
 
0.6%
0.6756867
 
0.6%
152886
 
0.5%
443015
 
0.4%
2.6735282
 
0.4%
3.6732629
 
0.3%
Other values (63)166547
 
1.7%
ValueCountFrequency (%)
0.339295245
93.0%
0.6756867
 
0.6%
152886
 
0.5%
1.3357614
 
0.6%
1.6763626
 
0.6%
296547
 
1.0%
2.3399742
 
1.0%
2.6735282
 
0.4%
331626
 
0.3%
3.3331834
 
0.3%
ValueCountFrequency (%)
25.676
 
< 0.1%
25.3315
 
< 0.1%
2519
 
< 0.1%
2415
 
< 0.1%
236
 
< 0.1%
22.6736
 
< 0.1%
22.3314
 
< 0.1%
2239
< 0.1%
21.6728
 
< 0.1%
21.3390
< 0.1%

pair_id
Real number (ℝ)

Distinct109
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.060716
Minimum0
Maximum124
Zeros26937
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size76.3 MiB
2025-11-25T21:34:25.576949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q132
median54
Q378
95-th percentile92
Maximum124
Range124
Interquartile range (IQR)46

Descriptive statistics

Standard deviation27.251264
Coefficient of variation (CV)0.50408625
Kurtosis-1.0957706
Mean54.060716
Median Absolute Deviation (MAD)24
Skewness-0.011629467
Sum5.4060716 × 108
Variance742.63137
MonotonicityNot monotonic
2025-11-25T21:34:25.725446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82229366
 
2.3%
70224932
 
2.2%
54215599
 
2.2%
25212538
 
2.1%
84205739
 
2.1%
30201341
 
2.0%
19189987
 
1.9%
73189514
 
1.9%
16184656
 
1.8%
33178176
 
1.8%
Other values (99)7968152
79.7%
ValueCountFrequency (%)
026937
 
0.3%
2157491
1.6%
4172050
1.7%
76546
 
0.1%
822685
 
0.2%
923028
 
0.2%
106547
 
0.1%
1124341
 
0.2%
1242046
 
0.4%
1316515
 
0.2%
ValueCountFrequency (%)
12428
 
< 0.1%
12362
 
< 0.1%
1214145
 
< 0.1%
120114
 
< 0.1%
11960
 
< 0.1%
1185077
 
0.1%
116127
 
< 0.1%
115302
 
< 0.1%
11432361
0.3%
1138844
 
0.1%

Interactions

2025-11-25T21:33:38.410596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:31:22.462703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:31:38.816568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:31:53.671095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:08.998271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:23.943268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:38.879930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:54.137531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:08.907161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:23.722361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:39.780646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:31:23.879829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:31:40.159091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:31:55.051091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:10.375977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:25.308566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:40.264387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:55.535242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:10.284100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:25.073958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:41.137761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:31:25.253275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:31:41.533143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:31:56.340750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:11.729449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:26.688029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:41.640460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:56.915681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:11.668812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:26.434541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:42.513011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:31:26.627117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:31:42.899686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:31:57.695374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:13.041235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:28.086204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:43.011997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:58.270872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:13.049382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:27.803492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:43.877888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:31:28.024631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:31:44.286798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:31:59.641860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:14.521260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:29.423600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:44.394149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:59.630660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:14.409150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:29.154053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:45.591727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:31:29.407373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:31:45.654135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:01.016725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:15.954299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:30.821331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:45.767622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:01.262688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:16.098853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:30.840096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:47.396483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:31:31.129534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:31:47.037187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:02.642882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:17.636961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:32.591688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:47.579794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:02.960498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:17.892711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:32.640581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:49.101639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:31:33.078815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:31:48.877161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:04.443344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:19.430133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:34.403872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:49.392997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:04.710407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:19.565470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:34.374962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:50.464115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:31:35.018413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:31:50.699089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:06.214070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:21.157054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:36.077202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:51.019549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:06.151987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:20.941649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:35.674726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:51.775868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:31:37.081681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:31:52.293960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:07.617634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:22.530793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:37.475457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:32:52.446526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:07.533546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:22.326515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T21:33:37.058617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-25T21:34:25.844208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
date_indexpair_idprobe_efficiencyreply_ratioroute_changedrtt_countrtt_meanrtt_rangertt_stdtime_since_lasttr_id
date_index1.0000.031-0.0580.0490.022-0.058-0.059-0.057-0.057-0.0200.924
pair_id0.0311.0000.041-0.0850.0290.041-0.0290.0420.043-0.0060.027
probe_efficiency-0.0580.0411.0000.1120.0211.000-0.1700.9980.9980.017-0.055
reply_ratio0.049-0.0850.1121.0000.0250.112-0.0920.1080.106-0.0120.042
route_changed0.0220.0290.0210.0251.0000.0210.0120.0100.0100.0380.021
rtt_count-0.0580.0411.0000.1120.0211.000-0.1700.9980.9980.017-0.055
rtt_mean-0.059-0.029-0.170-0.0920.012-0.1701.000-0.170-0.1700.027-0.054
rtt_range-0.0570.0420.9980.1080.0100.998-0.1701.0000.9990.017-0.055
rtt_std-0.0570.0430.9980.1060.0100.998-0.1700.9991.0000.017-0.055
time_since_last-0.020-0.0060.017-0.0120.0380.0170.0270.0170.0171.000-0.018
tr_id0.9240.027-0.0550.0420.021-0.055-0.054-0.055-0.055-0.0181.000

Missing values

2025-11-25T21:33:52.121734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-25T21:33:59.207250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

tr_idroute_changeddate_indexrtt_meanrtt_stdrtt_rangertt_counttime_since_lastreply_ratioprobe_efficiencypair_id
0001210.090.00.010.01.00.330
1101210.090.00.010.01.00.330
2201210.090.00.010.01.00.330
3301210.090.00.010.01.00.330
4401210.090.00.010.01.00.330
5501210.090.00.010.01.00.330
6601210.090.00.010.01.00.330
7701210.090.00.010.01.00.330
8801210.090.00.010.01.00.330
9901210.090.00.010.01.00.330
tr_idroute_changeddate_indexrtt_meanrtt_stdrtt_rangertt_counttime_since_lastreply_ratioprobe_efficiencypair_id
99999903469874025105.941.514.1960.01.02.0124
99999913469875025105.941.514.1960.01.02.0124
99999923469876025105.941.514.1960.01.02.0124
99999933469877025105.941.514.1960.01.02.0124
99999943469878025105.941.514.1960.01.02.0124
99999953469879025105.941.514.1960.01.02.0124
99999963469880025105.941.514.1960.01.02.0124
99999973469881025105.941.514.1960.01.02.0124
99999983469882025105.941.514.1960.01.02.0124
99999993469883025105.941.514.1960.01.02.0124